{"title"=>"Genome-wide prediction methods in highly diverse and heterozygous species: Proof-of-concept through simulation in grapevine", "type"=>"journal", "authors"=>[{"first_name"=>"Agota", "last_name"=>"Fodor", "scopus_author_id"=>"23984511900"}, {"first_name"=>"Vincent", "last_name"=>"Segura", "scopus_author_id"=>"12784789800"}, {"first_name"=>"Marie", "last_name"=>"Denis", "scopus_author_id"=>"55249820000"}, {"first_name"=>"Samuel", "last_name"=>"Neuenschwander", "scopus_author_id"=>"55503182800"}, {"first_name"=>"Alexandre", "last_name"=>"Fournier-Level", "scopus_author_id"=>"24278587800"}, {"first_name"=>"Philippe", "last_name"=>"Chatelet", "scopus_author_id"=>"16232683500"}, {"first_name"=>"Félix Abdel Aziz", "last_name"=>"Homa", "scopus_author_id"=>"56410734400"}, {"first_name"=>"Thierry", "last_name"=>"Lacombe", "scopus_author_id"=>"23485802300"}, {"first_name"=>"Patrice", "last_name"=>"This", "scopus_author_id"=>"55780314900"}, {"first_name"=>"Loic Le", "last_name"=>"Cunff", "scopus_author_id"=>"40461387900"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"600373127", "sgr"=>"84909606253", "pmid"=>"25365338", "scopus"=>"2-s2.0-84909606253", "isbn"=>"1932-6203", "doi"=>"10.1371/journal.pone.0110436", "issn"=>"19326203"}, "id"=>"a933dd5f-c2cf-381b-99e5-dbbc58fdce96", "abstract"=>"Nowadays, genome-wide association studies (GWAS) and genomic selection (GS) methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9) using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.", "link"=>"http://www.mendeley.com/research/genomewide-prediction-methods-highly-diverse-heterozygous-species-proofofconcept-through-simulation", "reader_count"=>46, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>16, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>16, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>3, "Agricultural and Biological Sciences"=>39, "Physics and Astronomy"=>1, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>39}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Uruguay"=>1, "Brazil"=>1, "Denmark"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>0}

{"files"=>["https://ndownloader.figshare.com/files/1777671"], "description"=>"<p>This scheme, implemented with quantiNemo, is composed of three steps: burn-in, domestication and breeding. Burn-in and domestication steps had the purpose to obtain grapevine diversity groups corresponding to Western Europe wine group (WW), Eastern Europe and Balkan wine group (WE) and Eastern Europe and Caucasus table group (TE) as described by <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110436#pone.0110436-Bacilieri1\" target=\"_blank\">[22]</a>. Breeding step models crosses between selected individuals of these groups. At the right side of the figure are represented generation numbers and historical events with dates. White area is representing wild grape, after domestication it is showed grey. “Wine” and “Table” symbolize the two different definitions of selection applied on the trait under selection (selection optima and intensity). Black arrows show the direction of migration and its intensity is indicated by boldface numbers, specifying the number of migrating individuals. The stringency of each bottleneck is indicated by specifying the number of selected individuals (in regular font).</p>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227763, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436.g001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scheme_of_the_demographical_scenario_based_on_our_working_hypothesis_on_grapevine_evolution_/1227763", "title"=>"Scheme of the demographical scenario based on our working hypothesis on grapevine evolution.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-03 03:57:09"}

{"files"=>["https://ndownloader.figshare.com/files/1777702"], "description"=>"<div><p>Nowadays, genome-wide association studies (GWAS) and genomic selection (GS) methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9) using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.</p></div>", "links"=>[], "tags"=>["GWAS", "training population", "training populations", "gs", "prediction accuracy", "snp", "diversity grapevine data", "model"], "article_id"=>1227794, "categories"=>["Biological Sciences"], "users"=>["Agota Fodor", "Vincent Segura", "Marie Denis", "Samuel Neuenschwander", "Alexandre Fournier-Level", "Philippe Chatelet", "Félix Abdel Aziz Homa", "Thierry Lacombe", "Patrice This", "Loïc Le Cunff"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110436", "stats"=>{"downloads"=>14, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genome_Wide_Prediction_Methods_in_Highly_Diverse_and_Heterozygous_Species_Proof_of_Concept_through_Simulation_in_Grapevine_/1227794", "title"=>"Genome-Wide Prediction Methods in Highly Diverse and Heterozygous Species: Proof-of-Concept through Simulation in Grapevine", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-03 03:57:09"}